pytorch的nn.MSELoss损失函数

MSE是mean squared error的缩写，即平均平方误差，简称均方误差。

MSE是逐元素计算的，计算公式为：

旧版的nn.MSELoss()函数有reduce、size_average两个参数，新版的只有一个reduction参数了，功能是一样的。reduction的意思是维度要不要缩减，以及怎么缩减，有三个选项：

• \'none\': no reduction will be applied.
• \'mean\': the sum of the output will be divided by the number of elements in the output.
• \'sum\': the output will be summed.

如果不设置reduction参数，默认是\'mean\'。

程序示例： 

import torch
import torch.nn as nn

a = torch.tensor([[1, 2], [3, 4]], dtype=torch.float)
b = torch.tensor([[3, 5], [8, 6]], dtype=torch.float)

loss_fn1 = torch.nn.MSELoss(reduction=\'none\')
loss1 = loss_fn1(a.float(), b.float())
print(loss1)   # 输出结果：tensor([[ 4.,  9.],
               #                 [25.,  4.]])

loss_fn2 = torch.nn.MSELoss(reduction=\'sum\')
loss2 = loss_fn2(a.float(), b.float())
print(loss2)   # 输出结果：tensor(42.)


loss_fn3 = torch.nn.MSELoss(reduction=\'mean\')
loss3 = loss_fn3(a.float(), b.float())
print(loss3)   # 输出结果：tensor(10.5000)

 

对于三维的输入也是一样的：

a = torch.randint(0, 9, (2, 2, 3)).float()
b = torch.randint(0, 9, (2, 2, 3)).float()
print(\'a:\n\', a)
print(\'b:\n\', b)

loss_fn1 = torch.nn.MSELoss(reduction=\'none\')
loss1 = loss_fn1(a.float(), b.float())
print(\'loss_none:\n\', loss1)

loss_fn2 = torch.nn.MSELoss(reduction=\'sum\')
loss2 = loss_fn2(a.float(), b.float())
print(\'loss_sum:\n\', loss2)


loss_fn3 = torch.nn.MSELoss(reduction=\'mean\')
loss3 = loss_fn3(a.float(), b.float())
print(\'loss_mean:\n\', loss3)

运行结果：

 

 参考资料：

pytorch的nn.MSELoss损失函数